Home
Help
Resources
Extensions
FAQ
NetLogo Publications
Donate

Models:
Library
Community
Modeling Commons

User Manuals:
Web
Printable
Chinese
Czech
Farsi / Persian
Japanese
Spanish

## NetLogo User Community Models

## WHAT IS IT?

A variant of the old garbage-can model (Cohen, Olsen, March 1972) where deciders (workers) have skills instead of energy and where problems (tasks) do not request energy to be solved but skills. And the choices that can be used by deciders to solve problems are in a way hidden in the skills of the deciders. Moreover workers can

## HOW IT WORKS

Tasks require different skills to be performed (for instance, technical, legal and managerial skills), and agents have the respective skills to different extents.

Currently, three different skills are defined. The extent to which a certain task require agents to have the respective skills is expressed in a triple of numbers between 0 and 1. Similarly, the agents' skills are also defined as triples of numvers between 0 and 1.

Idle agents (white) look around to find tasks that they can perform, according to one out of four organisationwide task allocation rules, comparing their own skills to the skills required by the tasks (red) they observe. When they decide to accept a task (which then turns blue) they start working on it (and turn green). In every time step (round) the amount of skill required to perform the task is decreased by the respective amount of skill the agent has, such that it takes one or more rounds to perform the task (the task then vanishes, but the worker's colour remains green until the next round when it either becomes white when no new task is found or remains green when the next task is found immediately).

All four task allocation rules are based on the concept of some matching between the skills of an agent and the skills required to perform a task. The simplest rule is "random" (i.e. no match is aimed at), the rule "optimal-eucild" minimises the Eucildean distance between the two vectors describing the agent's and the task's skills, the rule "optimal-chebyshev" minimises the respective Chebyshev distance between the two vectors, and the rule "cosine" minimises the angle between the two vectors.

Thus, for instance, if an agent has skills (.1 .2 .3) and decides to perform a task that requires skills (.2 .2 .7) than it takes this agent three rounds to perform this task (after round one the remaining requested skills are decreased to (.1 .0 .4), after round two to (.0 .0 .1) and after round three the task is fulfilled.

If the switch 'team' is turned on, workers can ask other workers for help when they find
that it would take them too long to perform the selected task. Worker and helper are connected by a directed link while their cooperation continues, and the worker turns red, while the helper turns yellow.

## HOW TO USE IT

As agents can find tasks only within the range of their vision, it is necessary to define the minimum and maximum ranges of vision. Individual agents then have ranges of vision uniformly distributed between these two values.

New tasks come into being at the beginning of each round, and one can input the number of tasks arising per period.

## THINGS TO NOTICE

One will soon notice that the performance of the organisation does not only depend on the speed of the task inflow, but more so on its task allocation rule and whether workers can or cannot form two-person teams.

## THINGS TO TRY

Task allocations rules should be changed between different runs, and for the same task allocation rules the task inflow parameter should be changed in a wide range (14 to 22 turned out most interesting).

## EXTENDING THE MODEL

One addition has been made in a companion model called EvTaskAlloc.nlogo, where the individual agents select tasks according to their individual selection rules and learn to change their rules according to their own and their colleagues' experience.

## NETLOGO FEATURES

No particularly interesting features of NetLogo were used.

## RELATED MODELS